reservoirpy.nodes.ScikitLearnNode#

class reservoirpy.nodes.ScikitLearnNode(

model,

output_dim: int | None = None,

name: str | None = None,

**kwargs,

)[source]#

A node interfacing a scikit-learn linear model that can be used as an offline readout node.

The ScikitLearnNode takes a scikit-learn model as parameter and creates a node with the specified model.

We currently support classifiers (like sklearn.linear_model.LogisticRegression or sklearn.linear_model.RidgeClassifier) and regressors (like sklearn.linear_model.Lasso or sklearn.linear_model.ElasticNet).

For more information on the above-mentioned estimators, please visit scikit-learn linear model API reference

Parameters:

model (class, scikit-learn model) – scikit-learn class to be wrapped by the Node.
output_dim (int, optional) – Number of units in the readout, can be inferred at first call.
name (str, optional) – Node name.
**kwargs – Additional keyword arguments passed to the scikit-learn model.

Example

>>> from reservoirpy.nodes import Reservoir, ScikitLearnNode
>>> from sklearn.linear_model import Lasso
>>> reservoir = Reservoir(units=100)
>>> readout = ScikitLearnNode(model=Lasso, model_hypers={"alpha":1e-5})
>>> model = reservoir >> readout

Methods

`__init__`(model[, output_dim, name])
`fit`(x[, y, warmup])	Offline fitting method of a Node.
`initialize`(x[, y])	Define input and output dimensions, and instantiate variables.
`predict`([x, iters, workers])	Alias for `run()`
`reset`()	Reset all Node state
`run`([x, iters, workers])	Run the Node on a sequence of data.
`step`([x])	Call the Node function on a single step of data and update the state of the Node.

Attributes

`initialized`	True if the Node has been initialized
`input_dim`	Expected dimension of the Node input.
`name`	Optional name of the Node.
`output_dim`	Expected dimension of the Node input.
`model`	scikit-learn class to be wrapped by the Node.
`model_kwargs`	Additional keyword arguments passed to the scikit-learn model.
`instances`	Model instance or list of model instances if multiple output are expected and the scikit-learn model doesn't support it.
`state`	Current state of the Node.

Offline fitting method of a Node.

Parameters:

x (list or array-like of shape ([series, ] timesteps, input_dim), optional) – Input sequences dataset.
y (list or array-like of shape ([series], timesteps, output_dim), optional) – Teacher signals dataset. If None, the method will try to fit the Node in an unsupervised way, if possible.
warmup (int, default to 0) – Number of timesteps to consider as warmup and discard at the beginning of each timeseries before training.

Returns:

Node trained offline.

Return type:

Node

Define input and output dimensions, and instantiate variables.

Only called once, before fitting or running the node.

Parameters:

x (array of shape (input_dim,) or (timestep, input_dim)) – Input data to the node.
y (None) – Training data to the node. As it is not a trainable node, y is expected to be None.

initialized: bool = False#: True if the Node has been initialized

input_dim: int = None#: Expected dimension of the Node input. Can be None before initialization

instances: sklearn.base.BaseEstimator | list[sklearn.base.BaseEstimator]#: Model instance or list of model instances if multiple output are expected and the scikit-learn model doesn’t support it.

model: type[sklearn.base.BaseEstimator]#: scikit-learn class to be wrapped by the Node.

model_kwargs: dict[str, Any]#: Additional keyword arguments passed to the scikit-learn model.

name: str | None = None#: Optional name of the Node.

output_dim: int = None#: Expected dimension of the Node input. Can be None before initialization

Alias for run()

Run the Node on a sequence of data. Can update the state of the Node several times.

Parameters:

x (array-like of shape ([n_inputs,] timesteps, input_dim) or list of) – arrays of shape (timesteps, input_dim), optional A sequence of data of shape (timesteps, features).
iters (int, optional) – If x is None, a dimensionless timeseries of length iters is used instead.
workers (int, default to 1) – Number of workers used for parallelization. If set to -1, all available workers (threads or processes) are used.

Returns:

A sequence of output vectors.

Return type:

array of shape ([n_inputs,] timesteps, output_dim) or list of arrays

reset() → dict[str, ndarray][source]#

Reset all Node state

Returns:: dict[str, np.array]
Return type:: previous state of the Node.

Run the Node on a sequence of data. Can update the state of the Node several times.

Parameters:

x (array-like of shape ([n_inputs,] timesteps, input_dim) or list of arrays of shape (timesteps, input_dim), optional) – A timeseries, array of shape (timesteps, features), or a sequence of timeseries. Input of the Node.
iters (int, optional) – If x is None, a dimensionless timeseries of length iters is used instead.
workers (int, default to 1) – Number of workers used for parallelization. If set to -1, all available workers (threads or processes) are used.

Returns:

A sequence of output vectors.

Return type:

array of shape ([n_inputs,] timesteps, output_dim) or list of arrays

state: State#: Current state of the Node. Must have “out” as one of the keys.

step(x: array(d) | None = None)[source]#

Call the Node function on a single step of data and update the state of the Node.

Parameters:: x (array of shape (input_dim,), optional) – One single step of input data. If None, an empty array is used instead and the Node is assumed to have an input_dim of 0
Returns:: An output vector.
Return type:: array of shape (output_dim,)